Memristive systems offer biomimetic functions that are being actively explored for energy-efficient neuromorphic circuits. In addition to providing ultimate geometric scaling limits, 2D semiconductors enable unique gatetunable responses including the recent realization of hybrid memristor and transistor devices known as memtransistors. In particular, monolayer MoS 2 memtransistors exhibit nonvolatile memristive switching where the resistance of each state is modulated by a gate terminal. Here, further control over the memtransistor neuromorphic response through the introduction of a second gate terminal is gained. The resulting dual-gated memtransistors allow tunability over the learning rate for non-Hebbian training where the long-term potentiation and depression synaptic behavior is dictated by gate biases during the reading and writing processes. Furthermore, the electrostatic control provided by dual gates provides a compact solution to the sneak current problem in traditional memristor crossbar arrays. In this manner, dual gating facilitates the full utilization and integration of memtransistor functionality in highly scaled crossbar circuits. Furthermore, the tunability of long-term potentiation yields improved linearity and symmetry of weight update rules that are utilized in simulated artificial neural networks to achieve a 94% recognition rate of handwritten digits.

electronic, [3-4,4] and functional requirements. [5] Conventional silicon integrated circuit (IC) technology faces significant challenges in meeting these demands due to its limited mechanical flexibility, high temperature processing, and scaling limitations. Emerging alternative computing platforms based on other crystalline semiconductors suffer from similar limitations. Consequently, nextgeneration computing necessitates the exploration of radically different electronic materials. Single-walled carbon nanotubes (SWCNTs) are among the most promising and highly studied nanoelectronic materials. Due to their small size, [6,7] solutionprocessability, [8] chemical stability, [9] and chirality-dependent optoelectronic properties, [10] SWCNTs offer a number of unique advantages and are compatible with the complex requirements of future computing devices. Recent advances in chiral enrichment of polydisperse SWCNTs [8,10] have allowed their use as semiconducting channels in diverse settings including charge transport devices, [2] optical emitters and detectors, [11,12] and chemical sensors. [2,3,13,14] With this tunable functionality, a range of SWCNT-based computing applications have been realized, such as printed digital logic, [15] sub-10 nm complementary metal-oxide-semiconductor (CMOS) field-effect transistors (FETs), [16] neuromorphic devices, [17] single-photon emitters (SPE), [18] and enantiomer-recognition sensors. [14] Herein, we discuss recent advances in SWCNT-based computing technologies that process, manage, and communicate information, with an emphasis on the enabling role of chiral enrichment. Section 2.1 defines the different levels of chiral enrichment. Sections 2.2 and 2.3 describe direct growth methods and post-processing purification of SWCNTs for electronic-type and monochiral enrichment. Section 3 discusses applications of electronic-type-enriched SWCNTs such as wearables, highly scaled FETs, 3D logic-memory integration, and neuromorphic devices. Section 4 outlines applications of monochiral-enriched SWCNTs including monochiral FETs, optical emitters, photodetectors, and optoelectronic ICs. Section 5 considers recent progress toward enantiomerically pure SWCNTs. Finally, Section 6 presents an outlook for SWCNTs in next-generation computing applications including a delineation of remaining challenges and future opportunities. For the past half century, silicon has served as the primary material platform for integrated circuit technology. However, the recent proliferation of nontraditional electronics, such as wearables, embedded systems, and lowpower portable devices, has led to increasingly complex mechanical and electrical performance requirements. Among emerging electronic materials, single-walled carbon nanotubes (SWCNTs) are promising candidates for next-generation computing as a result of their superlative electrical, optical, and mechanical properties. Moreover, their chirality-dependent properties enable a wide range of emerging electronic applications including sub-10 nm complementary fie...

Spiking neural networks exploit spatiotemporal processing, spiking sparsity, and high interneuron bandwidth to maximize the energy efficiency of neuromorphic computing. While conventional silicon-based technology can be used in this context, the resulting neuronsynapse circuits require multiple transistors and complicated layouts that limit integration density. Here, we demonstrate unprecedented electrostatic control of dual-gated Gaussian heterojunction transistors for simplified spiking neuron implementation. These devices employ wafer-scale mixed-dimensional van der Waals heterojunctions consisting of chemical vapor deposited monolayer molybdenum disulfide and solution-processed semiconducting single-walled carbon nanotubes to emulate the spike-generating ion channels in biological neurons. Circuits based on these dual-gated Gaussian devices enable a variety of biological spiking responses including phasic spiking, delayed spiking, and tonic bursting. In addition to neuromorphic computing, the tunable Gaussian response has significant implications for a range of other applications including telecommunications, computer vision, and natural language processing.

Artificial intelligence and machine learning are growing computing paradigms, but current algorithms incur undesirable energy costs on conventional hardware platforms, thus motivating the exploration of more efficient neuromorphic architectures. Toward this end, we introduce here a memtransistor with gate-tunable dynamic learning behavior. By fabricating memtransistors from monolayer MoS 2 grown on sapphire, the relative importance of the vertical field effect from the gate is enhanced, thereby heightening reconfigurability of the device response. Inspired by biological systems, gate pulses are used to modulate potentiation and depression, resulting in diverse learning curves and simplified spike-timing-dependent plasticity that facilitate unsupervised learning in simulated spiking neural networks. This capability also enables continuous learning, which is a previously underexplored cognitive concept in neuromorphic computing. Overall, this work demonstrates that the reconfigurability of memtransistors provides unique hardware accelerator opportunities for energy efficient artificial intelligence and machine learning.

Increasingly complex demonstrations of integrated circuit elements based on semiconducting single-walled carbon nanotubes (SWCNTs) mark the maturation of this technology for use in next-generation electronics. In particular, organic materials have recently been leveraged as dopant and encapsulation layers to enable stable SWCNT-based rail-to-rail, low-power complementary metal-oxide-semiconductor (CMOS) logic circuits. To explore the limits of this technology in extreme environments, here we study total ionizing dose (TID) effects in enhancement-mode SWCNT-CMOS inverters that employ organic doping and encapsulation layers. Details of the evolution of the device transport properties are revealed by in situ and in operando measurements, identifying n-type transistors as the more TID-sensitive component of the CMOS system with over an order of magnitude larger degradation of the static power dissipation. To further improve device stability, radiation-hardening approaches are explored, resulting in the observation that SWNCT-CMOS circuits are TID-hard under dynamic bias operation. Overall, this work reveals conditions under which SWCNTs can be employed for radiation-hard integrated circuits, thus presenting significant potential for next-generation satellite and space applications.